Detection of Fake News with RoBERTa Based Embedding and Modified Deep Neural Network Architecture

Rakib Mollah, MA; Kabir, MMJ; Kabir, M; Reza, MS

Detection of Fake News with RoBERTa Based Embedding and Modified Deep Neural Network Architecture

Rakib Mollah, MA Kabir, MMJ Kabir, M Reza, MS

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 26th International Conference on Computer and Information Technology (ICCIT), 2024, 00, pp. 1-6
Issue Date:: 2024-02-27

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Field	Value	Language
dc.contributor.author	Rakib Mollah, MA
dc.contributor.author	Kabir, MMJ
dc.contributor.author	Kabir, M
dc.contributor.author	Reza, MS
dc.date	2023-12-13
dc.date.accessioned	2025-03-12T21:53:01Z
dc.date.available	2025-03-12T21:53:01Z
dc.date.issued	2024-02-27
dc.identifier.citation	2023 26th International Conference on Computer and Information Technology (ICCIT), 2024, 00, pp. 1-6
dc.identifier.isbn	979-8-3503-5902-2
dc.identifier.uri	http://hdl.handle.net/10453/185743
dc.description.abstract	The spread of fake news has emerged as a critical challenge in the era of information and digital connectivity The consequences of misinformation can be profound affecting public opinion policy decisions and even public health Therefore detecting and reducing the spread of fake news is an essential issue that requires reliable and precise solutions Historically the field of fake news detection has witnessed notable advancements but several shortcomings have persisted Many earlier approaches struggled to attain the requisite levels of accuracy Another formidable obstacle that these earlier approaches encountered was the dynamic and ever evolving nature of the tactics employed by those who spread fake news They employ sensational language which uses phrases with high emotional content to attract readers and elicit a strong emotional response Recognizing these deficiencies we present an innovative solution that leverages the state of the art RoBERTa model and a meticulously modified deep neural network architecture Our approach stands out by not only recognizing the urgency of the fake news detection problem but also by proposing architectural enhancements It specifically targets the inadequacies of prior methods We introduce attention mechanisms designed to identify subtle cues indicative of misinformation The feature extraction techniques capture the nuanced patterns that fake news articles often follow These architectural refinements make our model extremely effective and achieve an accuracy of 99 76 The comprehensive evaluations demonstrate that our RoBERTa based model consistently outperforms previous state of the art fake news detection models emphasizing the crucial role of advanced language models in combating misinformation
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 26th International Conference on Computer and Information Technology (ICCIT)
dc.relation.ispartof	International Conference on Computer and Information Technology
dc.relation.isbasedon	10.1109/iccit60459.2023.10441206
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Detection of Fake News with RoBERTa Based Embedding and Modified Deep Neural Network Architecture
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Cox's Bazar, Bangladesh
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
pubs.organisational-group	University of Technology Sydney/Provost/TD School
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2025-03-12T21:52:59Z
pubs.finish-date	2023-12-15
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-12-13
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

The spread of fake news has emerged as a critical challenge in the era of information and digital connectivity The consequences of misinformation can be profound affecting public opinion policy decisions and even public health Therefore detecting and reducing the spread of fake news is an essential issue that requires reliable and precise solutions Historically the field of fake news detection has witnessed notable advancements but several shortcomings have persisted Many earlier approaches struggled to attain the requisite levels of accuracy Another formidable obstacle that these earlier approaches encountered was the dynamic and ever evolving nature of the tactics employed by those who spread fake news They employ sensational language which uses phrases with high emotional content to attract readers and elicit a strong emotional response Recognizing these deficiencies we present an innovative solution that leverages the state of the art RoBERTa model and a meticulously modified deep neural network architecture Our approach stands out by not only recognizing the urgency of the fake news detection problem but also by proposing architectural enhancements It specifically targets the inadequacies of prior methods We introduce attention mechanisms designed to identify subtle cues indicative of misinformation The feature extraction techniques capture the nuanced patterns that fake news articles often follow These architectural refinements make our model extremely effective and achieve an accuracy of 99 76 The comprehensive evaluations demonstrate that our RoBERTa based model consistently outperforms previous state of the art fake news detection models emphasizing the crucial role of advanced language models in combating misinformation

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/185743